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Discover how to leverage Wolfram System Modeler as a comprehensive training environment for artificial intelligence applications in this 29-minute session. Learn three advanced workflows that integrate physics-based modeling with machine learning techniques to enhance AI development. Master the process of training reinforcement learning agents within realistic simulated environments that mirror real-world conditions. Understand how to utilize high-fidelity models for generating training data to create neural surrogate models, enabling accelerated optimization processes. Explore the design and implementation of neural network-based model predictive controllers for managing complex physical systems. Gain insights into bridging the gap between traditional engineering models and intelligent algorithms, whether your objective is achieving faster simulations, developing smarter control systems, or creating more efficient AI training methodologies.
